Overview

Dataset statistics

Number of variables8
Number of observations895
Missing cells37
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.7 KiB
Average record size in memory67.1 B

Variable types

Numeric3
Categorical1
Text4

Dataset

Description공공기관 모바일 앱(공공앱) 운영 현황 (앱명,총 다운로드 수, 서비스 개시일 등)
Author행정안전부
URLhttps://www.data.go.kr/data/3034714/fileData.do

Alerts

연번 is highly overall correlated with 구분High correlation
총 다운로드 수 is highly overall correlated with 현 유지 수High correlation
현 유지 수 is highly overall correlated with 총 다운로드 수High correlation
구분 is highly overall correlated with 연번High correlation
현 유지 수 has 27 (3.0%) missing valuesMissing
연번 has unique valuesUnique
앱명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:40:46.781926
Analysis finished2023-12-12 23:40:48.423067
Duration1.64 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct895
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean448
Minimum1
Maximum895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2023-12-13T08:40:48.500703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.7
Q1224.5
median448
Q3671.5
95-th percentile850.3
Maximum895
Range894
Interquartile range (IQR)447

Descriptive statistics

Standard deviation258.50854
Coefficient of variation (CV)0.577028
Kurtosis-1.2
Mean448
Median Absolute Deviation (MAD)224
Skewness0
Sum400960
Variance66826.667
MonotonicityStrictly increasing
2023-12-13T08:40:48.644078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
2 1
 
0.1%
591 1
 
0.1%
592 1
 
0.1%
593 1
 
0.1%
594 1
 
0.1%
595 1
 
0.1%
596 1
 
0.1%
597 1
 
0.1%
598 1
 
0.1%
Other values (885) 885
98.9%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
895 1
0.1%
894 1
0.1%
893 1
0.1%
892 1
0.1%
891 1
0.1%
890 1
0.1%
889 1
0.1%
888 1
0.1%
887 1
0.1%
886 1
0.1%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
지자체
436 
공공기관
243 
중앙
216 

Length

Max length4
Median length3
Mean length3.0301676
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중앙
2nd row중앙
3rd row중앙
4th row공공기관
5th row중앙

Common Values

ValueCountFrequency (%)
지자체 436
48.7%
공공기관 243
27.2%
중앙 216
24.1%

Length

2023-12-13T08:40:48.777447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:40:48.879303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지자체 436
48.7%
공공기관 243
27.2%
중앙 216
24.1%

상위기관명
Text

Distinct70
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-13T08:40:49.088562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.8067039
Min length3

Characters and Unicode

Total characters4302
Distinct characters111
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.2%

Sample

1st row기상청
2nd row기상청
3rd row기상청
4th row경찰청
5th row경찰청
ValueCountFrequency (%)
서울특별시 72
 
8.0%
경기도 72
 
8.0%
경상북도 58
 
6.5%
교육부 43
 
4.8%
경상남도 41
 
4.6%
국토교통부 37
 
4.1%
과학기술정보통신부 33
 
3.7%
전라남도 26
 
2.9%
문화체육관광부 26
 
2.9%
보건복지부 25
 
2.8%
Other values (60) 463
51.7%
2023-12-13T08:40:49.402692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 338
 
7.9%
도 303
 
7.0%
경 200
 
4.6%
시 163
 
3.8%
청 127
 
3.0%
기 126
 
2.9%
광 124
 
2.9%
상 123
 
2.9%
교 113
 
2.6%
북 108
 
2.5%
Other values (101) 2577
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4301
> 99.9%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 338
 
7.9%
도 303
 
7.0%
경 200
 
4.7%
시 163
 
3.8%
청 127
 
3.0%
기 126
 
2.9%
광 124
 
2.9%
상 123
 
2.9%
교 113
 
2.6%
북 108
 
2.5%
Other values (100) 2576
59.9%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4301
> 99.9%
Common 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 338
 
7.9%
도 303
 
7.0%
경 200
 
4.7%
시 163
 
3.8%
청 127
 
3.0%
기 126
 
2.9%
광 124
 
2.9%
상 123
 
2.9%
교 113
 
2.6%
북 108
 
2.5%
Other values (100) 2576
59.9%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4301
> 99.9%
ASCII 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 338
 
7.9%
도 303
 
7.0%
경 200
 
4.7%
시 163
 
3.8%
청 127
 
3.0%
기 126
 
2.9%
광 124
 
2.9%
상 123
 
2.9%
교 113
 
2.6%
북 108
 
2.5%
Other values (100) 2576
59.9%
ASCII
ValueCountFrequency (%)
1
100.0%

기관명
Text

Distinct391
Distinct (%)43.7%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-13T08:40:49.608860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length5.5106145
Min length2

Characters and Unicode

Total characters4932
Distinct characters237
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique221 ?
Unique (%)24.7%

Sample

1st row관측정책과
2nd row기상레이더센터
3rd row항공기상청
4th row도로교통공단
5th row경찰청
ValueCountFrequency (%)
서울특별시 33
 
3.6%
국방부 21
 
2.3%
국립특수교육원 19
 
2.1%
한국직업능력개발원 14
 
1.5%
경주시 12
 
1.3%
양산시 11
 
1.2%
광주광역시 10
 
1.1%
부산광역시 10
 
1.1%
보건복지부 9
 
1.0%
국토교통부 9
 
1.0%
Other values (382) 769
83.9%
2023-12-13T08:40:49.943010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 282
 
5.7%
국 268
 
5.4%
원 201
 
4.1%
한 153
 
3.1%
공 139
 
2.8%
부 105
 
2.1%
교 104
 
2.1%
구 103
 
2.1%
산 100
 
2.0%
도 87
 
1.8%
Other values (227) 3390
68.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4901
99.4%
Space Separator 22
 
0.4%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%
Uppercase Letter 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 282
 
5.8%
국 268
 
5.5%
원 201
 
4.1%
한 153
 
3.1%
공 139
 
2.8%
부 105
 
2.1%
교 104
 
2.1%
구 103
 
2.1%
산 100
 
2.0%
도 87
 
1.8%
Other values (221) 3359
68.5%
Uppercase Letter
ValueCountFrequency (%)
B 1
33.3%
D 1
33.3%
K 1
33.3%
Space Separator
ValueCountFrequency (%)
22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4901
99.4%
Common 28
 
0.6%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 282
 
5.8%
국 268
 
5.5%
원 201
 
4.1%
한 153
 
3.1%
공 139
 
2.8%
부 105
 
2.1%
교 104
 
2.1%
구 103
 
2.1%
산 100
 
2.0%
도 87
 
1.8%
Other values (221) 3359
68.5%
Common
ValueCountFrequency (%)
22
78.6%
) 3
 
10.7%
( 3
 
10.7%
Latin
ValueCountFrequency (%)
B 1
33.3%
D 1
33.3%
K 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4901
99.4%
ASCII 31
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 282
 
5.8%
국 268
 
5.5%
원 201
 
4.1%
한 153
 
3.1%
공 139
 
2.8%
부 105
 
2.1%
교 104
 
2.1%
구 103
 
2.1%
산 100
 
2.0%
도 87
 
1.8%
Other values (221) 3359
68.5%
ASCII
ValueCountFrequency (%)
22
71.0%
) 3
 
9.7%
( 3
 
9.7%
B 1
 
3.2%
D 1
 
3.2%
K 1
 
3.2%

앱명
Text

UNIQUE 

Distinct895
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-13T08:40:50.137563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length29
Mean length8.5608939
Min length3

Characters and Unicode

Total characters7662
Distinct characters571
Distinct categories11 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique895 ?
Unique (%)100.0%

Sample

1st row기상청날씨제보시스템2.0
2nd row우리동네레이더날씨알리미
3rd row항공기상청
4th row스마트운전면허
5th row경찰청폴-안티스파이
ValueCountFrequency (%)
기상청날씨제보시스템2.0 1
 
0.1%
독도(dokdo 1
 
0.1%
청송아이맘 1
 
0.1%
영주시보건소-아이맘 1
 
0.1%
영주시스토리텔링-영스클럽 1
 
0.1%
영천시보건소아이맘 1
 
0.1%
울릉군보건의료원아이맘 1
 
0.1%
울진여행 1
 
0.1%
울진군청스마트알리미 1
 
0.1%
울진군보건소아이맘 1
 
0.1%
Other values (891) 891
98.9%
2023-12-13T08:40:50.495147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 171
 
2.2%
스 155
 
2.0%
보 130
 
1.7%
서 120
 
1.6%
이 108
 
1.4%
안 107
 
1.4%
정 107
 
1.4%
전 105
 
1.4%
관 99
 
1.3%
도 93
 
1.2%
Other values (561) 6467
84.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6360
83.0%
Uppercase Letter 489
 
6.4%
Lowercase Letter 465
 
6.1%
Decimal Number 101
 
1.3%
Open Punctuation 75
 
1.0%
Close Punctuation 75
 
1.0%
Dash Punctuation 56
 
0.7%
Other Punctuation 30
 
0.4%
Space Separator 6
 
0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 171
 
2.7%
스 155
 
2.4%
보 130
 
2.0%
서 120
 
1.9%
이 108
 
1.7%
안 107
 
1.7%
정 107
 
1.7%
전 105
 
1.7%
관 99
 
1.6%
도 93
 
1.5%
Other values (484) 5165
81.2%
Uppercase Letter
ValueCountFrequency (%)
S 44
 
9.0%
E 42
 
8.6%
K 36
 
7.4%
T 36
 
7.4%
I 35
 
7.2%
N 34
 
7.0%
B 32
 
6.5%
O 26
 
5.3%
R 25
 
5.1%
A 23
 
4.7%
Other values (16) 156
31.9%
Lowercase Letter
ValueCountFrequency (%)
e 70
15.1%
o 53
11.4%
a 53
11.4%
r 36
 
7.7%
i 34
 
7.3%
t 32
 
6.9%
n 20
 
4.3%
u 19
 
4.1%
s 18
 
3.9%
d 16
 
3.4%
Other values (13) 114
24.5%
Decimal Number
ValueCountFrequency (%)
1 33
32.7%
0 22
21.8%
2 21
20.8%
4 6
 
5.9%
9 5
 
5.0%
3 5
 
5.0%
6 3
 
3.0%
5 3
 
3.0%
8 2
 
2.0%
7 1
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 13
43.3%
. 11
36.7%
/ 3
 
10.0%
& 1
 
3.3%
@ 1
 
3.3%
: 1
 
3.3%
Open Punctuation
ValueCountFrequency (%)
( 71
94.7%
[ 2
 
2.7%
「 2
 
2.7%
Close Punctuation
ValueCountFrequency (%)
) 71
94.7%
] 2
 
2.7%
」 2
 
2.7%
Math Symbol
ValueCountFrequency (%)
+ 1
33.3%
= 1
33.3%
~ 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6349
82.9%
Latin 954
 
12.5%
Common 348
 
4.5%
Han 8
 
0.1%
Katakana 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 171
 
2.7%
스 155
 
2.4%
보 130
 
2.0%
서 120
 
1.9%
이 108
 
1.7%
안 107
 
1.7%
정 107
 
1.7%
전 105
 
1.7%
관 99
 
1.6%
도 93
 
1.5%
Other values (473) 5154
81.2%
Latin
ValueCountFrequency (%)
e 70
 
7.3%
o 53
 
5.6%
a 53
 
5.6%
S 44
 
4.6%
E 42
 
4.4%
r 36
 
3.8%
K 36
 
3.8%
T 36
 
3.8%
I 35
 
3.7%
i 34
 
3.6%
Other values (39) 515
54.0%
Common
ValueCountFrequency (%)
( 71
20.4%
) 71
20.4%
- 56
16.1%
1 33
9.5%
0 22
 
6.3%
2 21
 
6.0%
, 13
 
3.7%
. 11
 
3.2%
4 6
 
1.7%
6
 
1.7%
Other values (18) 38
10.9%
Han
ValueCountFrequency (%)
樂 1
12.5%
休 1
12.5%
自 1
12.5%
助 1
12.5%
游 1
12.5%
海 1
12.5%
韓 1
12.5%
旅 1
12.5%
Katakana
ValueCountFrequency (%)
ド 1
33.3%
ガ 1
33.3%
イ 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6349
82.9%
ASCII 1298
 
16.9%
CJK 8
 
0.1%
None 4
 
0.1%
Katakana 3
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 171
 
2.7%
스 155
 
2.4%
보 130
 
2.0%
서 120
 
1.9%
이 108
 
1.7%
안 107
 
1.7%
정 107
 
1.7%
전 105
 
1.7%
관 99
 
1.6%
도 93
 
1.5%
Other values (473) 5154
81.2%
ASCII
ValueCountFrequency (%)
( 71
 
5.5%
) 71
 
5.5%
e 70
 
5.4%
- 56
 
4.3%
o 53
 
4.1%
a 53
 
4.1%
S 44
 
3.4%
E 42
 
3.2%
r 36
 
2.8%
K 36
 
2.8%
Other values (65) 766
59.0%
None
ValueCountFrequency (%)
「 2
50.0%
」 2
50.0%
CJK
ValueCountFrequency (%)
樂 1
12.5%
休 1
12.5%
自 1
12.5%
助 1
12.5%
游 1
12.5%
海 1
12.5%
韓 1
12.5%
旅 1
12.5%
Katakana
ValueCountFrequency (%)
ド 1
33.3%
ガ 1
33.3%
イ 1
33.3%

총 다운로드 수
Real number (ℝ)

HIGH CORRELATION 

Distinct866
Distinct (%)97.5%
Missing7
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean156894.15
Minimum12
Maximum23128595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2023-12-13T08:40:50.623445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile215.35
Q11935
median7851.5
Q342271.25
95-th percentile602706
Maximum23128595
Range23128583
Interquartile range (IQR)40336.25

Descriptive statistics

Standard deviation974775.18
Coefficient of variation (CV)6.2129481
Kurtosis378.23049
Mean156894.15
Median Absolute Deviation (MAD)7195.5
Skewness17.625304
Sum1.39322 × 108
Variance9.5018666 × 1011
MonotonicityNot monotonic
2023-12-13T08:40:50.765437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 3
 
0.3%
500 2
 
0.2%
831 2
 
0.2%
158 2
 
0.2%
150 2
 
0.2%
1887 2
 
0.2%
901 2
 
0.2%
8811 2
 
0.2%
1042 2
 
0.2%
2297 2
 
0.2%
Other values (856) 867
96.9%
(Missing) 7
 
0.8%
ValueCountFrequency (%)
12 2
0.2%
18 1
0.1%
34 1
0.1%
38 1
0.1%
50 1
0.1%
53 1
0.1%
54 1
0.1%
56 1
0.1%
60 1
0.1%
67 1
0.1%
ValueCountFrequency (%)
23128595 1
0.1%
12486751 1
0.1%
5398249 1
0.1%
4050971 1
0.1%
3543027 1
0.1%
3536944 1
0.1%
3343284 1
0.1%
2929005 1
0.1%
2922657 1
0.1%
2902927 1
0.1%

현 유지 수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct769
Distinct (%)88.6%
Missing27
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean34718.81
Minimum1
Maximum4972987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2023-12-13T08:40:50.880276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile40.35
Q1336.5
median1420.5
Q38499
95-th percentile131293.1
Maximum4972987
Range4972986
Interquartile range (IQR)8162.5

Descriptive statistics

Standard deviation219999.98
Coefficient of variation (CV)6.336622
Kurtosis323.05283
Mean34718.81
Median Absolute Deviation (MAD)1324.5
Skewness16.230064
Sum30135927
Variance4.839999 × 1010
MonotonicityNot monotonic
2023-12-13T08:40:51.030501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 5
 
0.6%
40 4
 
0.4%
30 4
 
0.4%
68 3
 
0.3%
966 3
 
0.3%
27 3
 
0.3%
730 3
 
0.3%
338 3
 
0.3%
456 3
 
0.3%
61 3
 
0.3%
Other values (759) 834
93.2%
(Missing) 27
 
3.0%
ValueCountFrequency (%)
1 1
 
0.1%
5 1
 
0.1%
10 2
0.2%
11 2
0.2%
12 2
0.2%
13 3
0.3%
14 1
 
0.1%
15 2
0.2%
18 2
0.2%
19 2
0.2%
ValueCountFrequency (%)
4972987 1
0.1%
2633292 1
0.1%
1936450 1
0.1%
1292475 1
0.1%
945821 1
0.1%
832758 1
0.1%
729098 1
0.1%
600180 1
0.1%
572417 1
0.1%
563465 1
0.1%

서비스 개시일
Text

Distinct430
Distinct (%)48.2%
Missing3
Missing (%)0.3%
Memory size7.1 KiB
2023-12-13T08:40:51.287927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9955157
Min length6

Characters and Unicode

Total characters8916
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique315 ?
Unique (%)35.3%

Sample

1st row2014-03-01
2nd row2014-12-01
3rd row2012-04-09
4th row2012-08-01
5th row2014-08-27
ValueCountFrequency (%)
2014-12-01 19
 
2.1%
2013-01-01 16
 
1.8%
2015-12-01 15
 
1.7%
2016-03-01 13
 
1.5%
2016-01-01 13
 
1.5%
2016-02-01 13
 
1.5%
2014-01-01 11
 
1.2%
2014-11-01 11
 
1.2%
2014-02-01 10
 
1.1%
2016-05-01 10
 
1.1%
Other values (420) 761
85.3%
2023-12-13T08:40:51.667029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2284
25.6%
1 2019
22.6%
- 1783
20.0%
2 1379
15.5%
3 293
 
3.3%
5 267
 
3.0%
4 265
 
3.0%
6 258
 
2.9%
7 179
 
2.0%
9 98
 
1.1%
Other values (4) 91
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7130
80.0%
Dash Punctuation 1783
 
20.0%
Lowercase Letter 2
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2284
32.0%
1 2019
28.3%
2 1379
19.3%
3 293
 
4.1%
5 267
 
3.7%
4 265
 
3.7%
6 258
 
3.6%
7 179
 
2.5%
9 98
 
1.4%
8 88
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
n 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 1783
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8913
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2284
25.6%
1 2019
22.7%
- 1783
20.0%
2 1379
15.5%
3 293
 
3.3%
5 267
 
3.0%
4 265
 
3.0%
6 258
 
2.9%
7 179
 
2.0%
9 98
 
1.1%
Latin
ValueCountFrequency (%)
J 1
33.3%
a 1
33.3%
n 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2284
25.6%
1 2019
22.6%
- 1783
20.0%
2 1379
15.5%
3 293
 
3.3%
5 267
 
3.0%
4 265
 
3.0%
6 258
 
2.9%
7 179
 
2.0%
9 98
 
1.1%
Other values (4) 91
 
1.0%

Interactions

2023-12-13T08:40:47.888371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:40:47.408883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:40:47.663164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:40:47.976496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:40:47.487869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:40:47.739265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:40:48.056325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:40:47.569896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:40:47.812676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:40:51.771811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분상위기관명총 다운로드 수현 유지 수
연번1.0000.7650.9970.0400.072
구분0.7651.0000.9460.0940.210
상위기관명0.9970.9461.0000.0000.376
총 다운로드 수0.0400.0940.0001.0000.938
현 유지 수0.0720.2100.3760.9381.000
2023-12-13T08:40:51.913467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번총 다운로드 수현 유지 수구분
연번1.000-0.321-0.2640.639
총 다운로드 수-0.3211.0000.8850.070
현 유지 수-0.2640.8851.0000.088
구분0.6390.0700.0881.000

Missing values

2023-12-13T08:40:48.181222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:40:48.290673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T08:40:48.377860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번구분상위기관명기관명앱명총 다운로드 수현 유지 수서비스 개시일
01중앙기상청관측정책과기상청날씨제보시스템2.010647285822014-03-01
12중앙기상청기상레이더센터우리동네레이더날씨알리미4710095452014-12-01
23중앙기상청항공기상청항공기상청5299499762012-04-09
34공공기관경찰청도로교통공단스마트운전면허334738963342012-08-01
45중앙경찰청경찰청경찰청폴-안티스파이12283171820582014-08-27
56공공기관경찰청도로교통공단TBN교통방송240579371732011-03-01
67중앙경찰청경찰청경찰청사이버캅679171784932014-06-11
78중앙경찰청경찰청117Chat758196458562015-02-25
89중앙경찰청경찰청112긴급신고앱503163574672013-01-01
910중앙경찰청경찰청스마트국민제보5072282348212015-04-13
연번구분상위기관명기관명앱명총 다운로드 수현 유지 수서비스 개시일
885886지자체충청북도충청북도모바일충북29722992012-04-01
886887지자체충청북도단양군단양군원터치군정소통(직원비상연락망)3973232016-12-07
887888지자체충청북도옥천군옥천군청직원연락망8524312016-10-04
888889지자체충청북도청주시청주시안전지키미19496682017-01-01
889890지자체충청북도청주시소통팔달전자문서유통시스템11537902017-01-01
890891지자체충청북도교육청중앙도서관충북도서관톡톡578418092014-01-16
891892지자체충청북도교육청교육정보원학교톡톡98824285942013-07-01
892893지자체충청북도교육청교육정보원충북소통알리미48298332822017-03-01
893894지자체충청북도교육청충청북도교육청위드다문화18182016-12-23
894895지자체경기도양주시양주팜팅88910652015-04-01